Obstacle analyzer, vehicle control system, and methods thereof

ABSTRACT

According to various aspects, an obstacle analyzer may include: one or more sensors configured to receive obstacle identification information representing one or more identification features of an obstacle and obstacle condition information associated with one or more conditions of the obstacle; and one or more processors configured to identify the obstacle based on the received obstacle identification information and generate an identification value corresponding to the identified obstacle, determine a rating value representing a risk potential of the identified obstacle based on the received obstacle condition information, and store the rating value assigned to the identification value of the identified obstacle in one or more memories.

TECHNICAL FIELD

Various aspects relate generally to an obstacle analyzer, a vehiclecontrol system, and methods thereof, e.g., method for analyzing anobstacle and a method for controlling a vehicle.

BACKGROUND

In general, modern vehicles may include various active and passiveassistance systems to assist during driving the vehicle. As an example,an emergency brake assist (EBA), also referred to as brake assist (BA orBAS) may be implemented in the vehicle. The emergency brake assist mayinclude a braking system that increases braking pressure in anemergency. The emergency may be a predicted collision of the vehiclewith another vehicle or with a fixed object, as for example, a wall, atree, etc. The vehicle may include one or more sensors and one or moreprocessors that may be configured to predict a frontal collision of thevehicle with an obstacle. Further, one or more autonomous vehiclemaneuvering functions may be implemented in a vehicle, e.g., to drivethe vehicle into a parking position, to follow another vehicle that isdriving ahead, to more or less autonomously drive the vehicle, asexamples.

BRIEF DESCRIPTION OF THE DRAWINGS

Throughout the drawings, it should be noted that like reference numbersare used to depict the same or similar elements, features, andstructures. The drawings are not necessarily to scale, emphasis insteadgenerally being placed upon illustrating aspects of the disclosure. Inthe following description, some aspects of the disclosure are describedwith reference to the following drawings, in which:

FIG. 1 shows an exemplary obstacle analyzer in a schematic view,according to some aspects;

FIG. 2 shows an exemplary vehicle control system, according to someaspects;

FIG. 3 shows an exemplary vehicle including at least one of an obstacleanalyzer or a vehicle control system, according to some aspects;

FIG. 4 shows an exemplary traffic situation with a vehicle and variousother vehicles, according to some aspects;

FIG. 5 shows an exemplary flow diagram of a method for analyzing anobstacle, according to some aspects;

FIG. 6 shows an exemplary flow diagram of a method for controlling avehicle, according to some aspects;

FIG. 7A, FIG. 7B and FIG. 7C show exemplarily various characteristicfeatures related to tires of an analyzed vehicle, according to someaspects;

FIG. 8 shows various characteristic features related to damages of ananalyzed vehicle, according to some aspects;

FIG. 9 shows exemplarily an image of a car, a depth image of a car, anda 3D reference model of a car, according to some aspects;

FIG. 10A, FIG. 10B and FIG. 10C show exemplarily various flow diagram ofanalyzing a vehicle, a driver of a vehicle, and a passenger and/or cargoof a vehicle, according to some aspects;

FIG. 11 shows exemplarily a flow diagram of calculating a vehicle riskvalue, according to some aspects;

FIG. 12 shows exemplarily a flow diagram of calculating a driver riskvalue, according to some aspects; and

FIG. 13 shows exemplarily a flow diagram of determining an action basedon the calculated vehicle risk value and driver risk value, according tosome aspects.

DESCRIPTION

The following detailed description refers to the accompanying drawingsthat show, by way of illustration, specific details and aspects in whichthe disclosure may be practiced. These aspects are described insufficient detail to enable those skilled in the art to practice thedisclosure. Other aspects may be utilized and structural, logical, andelectrical changes may be made without departing from the scope of thedisclosure. The various aspects are not necessarily mutually exclusive,as some aspects can be combined with one or more other aspects to formnew aspects. Various aspects are described in connection with methodsand various aspects are described in connection with devices. However,it may be understood that aspects described in connection with methodsmay similarly apply to the devices, and vice versa.

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration”. Any aspect or design described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs.

The terms “at least one” and “one or more” may be understood to includea numerical quantity greater than or equal to one (e.g., one, two,three, four, [ . . . ], etc.). The term “a plurality” may be understoodto include a numerical quantity greater than or equal to two (e.g., two,three, four, five, [ . . . ], etc.).

The phrase “at least one of” with regard to a group of elements may beused herein to mean at least one element from the group consisting ofthe elements. For example, the phrase “at least one of” with regard to agroup of elements may be used herein to mean a selection of: one of thelisted elements, a plurality of one of the listed elements, a pluralityof individual listed elements, or a plurality of a multiple of listedelements.

The words “plural” and “multiple” in the description and the claimsexpressly refer to a quantity greater than one. Accordingly, any phrasesexplicitly invoking the aforementioned words (e.g., “a plurality of(objects)”, “multiple (objects)”) referring to a quantity of objectsexpressly refers more than one of the said objects. The terms “group(of)”, “set (of)”, “collection (of)”, “series (of)”, “sequence (of)”,“grouping (of)”, etc., and the like in the description and in theclaims, if any, refer to a quantity equal to or greater than one, i.e.one or more.

The term “data” as used herein may be understood to include informationin any suitable analog or digital form, e.g., provided as a file, aportion of a file, a set of files, a signal or stream, a portion of asignal or stream, a set of signals or streams, and the like. Further,the term “data” may also be used to mean a reference to information,e.g., in form of a pointer. The term “data”, however, is not limited tothe aforementioned examples and may take various forms and represent anyinformation as understood in the art.

The term “processor” as, for example, used herein may be understood asany kind of entity that allows handling data. The data may be handledaccording to one or more specific functions executed by the processor.Further, a processor as used herein may be understood as any kind ofcircuit, e.g., any kind of analog or digital circuit. The term “handle”or “handling” as for example used herein referring to data handling,file handling or request handling may be understood as any kind ofoperation, e.g., an I/O operation, and/or any kind of logic operation.An I/O operation may include, for example, storing (also referred to aswriting) and reading.

A processor may thus be or include an analog circuit, digital circuit,mixed-signal circuit, logic circuit, microprocessor, Central ProcessingUnit (CPU), Graphics Processing Unit (GPU), Digital Signal Processor(DSP), Field Programmable Gate Array (FPGA), integrated circuit,Application Specific Integrated Circuit (ASIC), etc., or any combinationthereof. Any other kind of implementation of the respective functions,which will be described below in further detail, may also be understoodas a processor, controller, or logic circuit. It is understood that anytwo (or more) of the processors, controllers, or logic circuits detailedherein may be realized as a single entity with equivalent functionalityor the like, and conversely that any single processor, controller, orlogic circuit detailed herein may be realized as two (or more) separateentities with equivalent functionality or the like.

Differences between software and hardware implemented data handling mayblur. A processor, controller, and/or circuit detailed herein may beimplemented in software, hardware and/or as hybrid implementationincluding software and hardware.

The term “system” (e.g., a computing system, a control system, etc.)detailed herein may be understood as a set of interacting elements,wherein the elements can be, by way of example and not of limitation,one or more mechanical components, one or more electrical components,one or more instructions (e.g., encoded in storage media), and/or one ormore processors, and the like.

As used herein, the term “memory”, and the like may be understood as anon-transitory computer-readable medium in which data or information canbe stored for retrieval. References to “memory” included herein may thusbe understood as referring to volatile or non-volatile memory, includingrandom access memory (RAM), read-only memory (ROM), flash memory,solid-state storage, magnetic tape, hard disk drive, optical drive,etc., or any combination thereof. Furthermore, it is appreciated thatregisters, shift registers, processor registers, data buffers, etc., arealso embraced herein by the term memory. It is appreciated that a singlecomponent referred to as “memory” or “a memory” may be composed of morethan one different type of memory, and thus may refer to a collectivecomponent including one or more types of memory. It is readilyunderstood that any single memory component may be separated intomultiple collectively equivalent memory components, and vice versa.

The term “vehicle” as used herein may be understood as any suitable typeof vehicle, e.g., any type of ground vehicle, a watercraft, an aircraft,or any other type of vehicle. In some aspects, the vehicle may be amotor vehicle (also referred to as automotive vehicle). As an example, avehicle may be a car also referred to as a motor car, a passenger car,etc. As another example, a vehicle may be a truck (also referred to asmotor truck), a van, etc. In other aspects, the vehicle may be apartially or fully autonomously flying drone (e.g. an aeronautical taxi)having, for example, a pilot and/or one or more passengers onboard.

The term “lane” with the meaning of a “driving lane” as used herein maybe understood as any type of solid infrastructure (or section thereof)on which a vehicle may drive. In a similar way, lanes may be associatedwith aeronautic traffic, marine traffic, etc., as well.

According to various aspects, information (e.g., obstacle identificationinformation, obstacle condition information, etc.) may be handled (e.g.,processed, analyzed, stored, etc.) in any suitable form, e.g., data mayrepresent the information and may be handled via a computing system. Theobstacle condition may be used herein with the meaning of any detectablecharacteristic of the obstacle itself and/or associated with theobstacle. As an example, in the case that the obstacle is a vehicle, adriver, a passenger, a load, etc., may be associated with the vehicle. Arisk that originates from a person or object that is associated with theobstacle, may be treated in the analysis (as described herein) as a riskpotential assigned to the obstacle.

In some aspects, one or more range imaging sensors may be used forsensing obstacles and/or persons and/or objects that are associated withan obstacle in the vicinity of the one or more imaging sensors. A rangeimaging sensor may allow associating range information (or in otherwords distance information or depth information) with an image, e.g., toprovide a range image having range data associated with pixel data ofthe image. This allows, for example, providing a range image of thevicinity of a vehicle including range information about one or moreobjects depicted in the image. The range information may include, forexample, one or more colors, one or more shadings associated with arelative distance from the range image sensor, etc. According to variousaspects, position data associated with positions of objects relative tothe vehicle and/or relative to an assembly of the vehicle may bedetermined from the range information. According to various aspects, arange image may be obtained, for example, by a stereo camera, e.g.,calculated from two or more images having a different perspective.Three-dimensional coordinates of points on an object may be obtained,for example, by stereophotogrammetry, based on two or more photographicimages taken from different positions. However, a range image may begenerated based on images obtained via other types of cameras, e.g.,based on time-of-flight (ToF) measurements, etc. Further, in someaspects, a range image may be merged with additional sensor data, e.g.,with sensor data of one or more radar sensors, etc.

In one or more aspects, a driving operation (such as, for example, anytype of safety operation, e.g., a collision avoidance function, a safetydistance keeping function, etc.) may be implemented via one or moreon-board components of a vehicle. The one or more on-board components ofthe vehicle may include, for example, a one or more cameras (e.g., atleast a front camera), a computer system, etc., in order to detectobstacles (e.g., at least in front of the vehicle) and to trigger anobstacle avoidance function (e.g., braking, etc.) to avoid a collisionwith the detected obstacles. The one or more on-board components of thevehicle may include, for example, a one or more cameras (e.g., at leasta front camera), a computer system, etc., in order to detect anothervehicle (e.g., at least in front of the vehicle) and to follow the othervehicle (e.g., autonomously) or at least to keep a predefined safetydistance with respect to the other vehicle.

In various aspects, a depth camera (or any other range image device) maybe used, for example, aligned at least in forward driving direction todetect during driving when an obstacle may come too close and wouldcause a collision with the vehicle. In a similar way, at least one depthcamera (or any other range image device) may be used, for example, thatis aligned in rear driving direction to avoid a collision in the casethat an obstacle approaches from this direction.

According to various aspects, one or more sensors and a computing systemmay be used to implement the functions described herein. The computingsystem may include, for example, one or more processors, one or morememories, etc. The computing system may be communicatively coupled tothe one or more sensors (e.g., of a vehicle) to obtain and analyzesensor data generated by the one or more sensors. According to someaspects, the one or more processors may be configured to generate depthimages in real-time from the data received from one or more rangeimaging sensors and analyze the depth image to find one or more featuresassociated with conditions that represent a risk potential.

Several aspects are described herein exemplarily with reference to amotor vehicle, wherein one more other vehicles represent obstacles in avicinity of the motor vehicle. However, other types of vehicles may beprovided including the same or similar structures and functions asdescribed exemplarily for the motor vehicle. Further, other obstaclesmay be considered in a similar way as described herein with reference tothe other vehicles.

In general, autonomous driving may be configured such that all vehiclesare treated equal. While it may make sense to treat every driveridentically in some scenarios, there may be certain circumstances thatshould influence the behavior of an autonomous driving function of avehicle based on the conditions of other vehicles and/or drivers ofother vehicles that are in a vicinity of the vehicle that may use theautonomous driving function.

A human driver may observe one or more other vehicles, objects, persons,etc., in traffic and estimate possible risks based this observation. If,for example, another car is driving in suspicious ways, a human drivermight assume that the other driver might be under the influence of drugsor might have other problems. As another example, a car in front of adriver may be damaged and/or held together, for example, by duct tape,wherein, in this case, a human driver may conclude that the damaged carand his driver might have been more frequently in accidents. In general,a human driver may tend to increase the safety distance from any othersuspicions car, object, person, etc.

In general, a conventional autonomous driving system may not be ablewith its artificial intelligence to distinct situations where a riskpotential may be present due to the condition of the one or more othervehicles in traffic. According to various aspects, an automatic systemis provided that may be used to assist autonomous driving functions. Theautomatic system may be configured to generate and/or use knowledge ofobstacles (e.g., other vehicles, objects, drivers, passengers,pedestrians, etc.) and adapt the own driving style accordingly.

According to various aspects, a detailed analysis of the status and/orshape of other drivers, vehicles, etc., in a surrounding of a vehiclemay be performed, e.g., to adjust an autonomous driving behavior of thevehicle. As an example, a greater safety distance may be kept in thecase that it is analyzed that a driver of another vehicle uses asmartphone while driving.

Using the obstacle analyzing approach as described herein, autonomousdriving may come closer to some of the benefits that have developed inhumans over many years. Illustratively, suspicion may be added into thepipeline of artificial intelligence for fully or partially autonomousdriving. As a result, one or more autonomous driving functions may beimproved, e.g., by adding a risk management based on an analysis ofother vehicles that could possibly harm the vehicle that uses the one ormore autonomous driving functions.

FIG. 1 illustrates an obstacle analyzer 100 in a schematic view,according to various aspects. The obstacle analyzer 100 may be used as apart of a control system of a vehicle, wherein the control system maycontrol and/or performs one or more partially or completely autonomousdriving operations, e.g., one or more partially or completely autonomoussafety operation, as example.

According to various aspects, the obstacle analyzer 100 may include oneor more sensors 102, one or more processors 104, and one or morememories 106. The one or more processors 104 and the one or morememories 106 may be part of a computing system 120. The computing system120 may be any suitable computing system implemented within a vehicle,e.g., within a motor vehicle. According to various aspects, the one ormore sensors 102 may include any sensor that is suitable for detectingpredefined features of an obstacle and/or of a person and/or an objectassociated with the obstacle. In a traffic situation, the obstacles maybe in some cases the other traffic participants, e.g., other vehicles.The one or more sensors 102 may include one or more image sensors toanalyze one or more obstacles (e.g., other vehicles) in a vicinity of avehicle. The one or more sensors 102 may include one or morehigh-resolution cameras (e.g., having a resolution of more than 1Megapixel, more than 2 Megapixel, more than 5 Megapixel, or more than 10Megapixel). The one or more sensors 102 may include one or morehigh-speed cameras (e.g., delivering more than 50 images per second,more than 100 images per second, or more than 200 images per second).The one or more high-speed cameras may have a high resolution as well.The one or more sensors 102 may include one or more depth cameras. Theone or more sensors 102 may include, e.g., in addition to one or moreimage-based sensors, one or more laser scanners, one or more radar(radio detection and ranging) sensors, one or more lidar (lightdetection and ranging) sensors, one or more ultrasonic sensors, one ormore acoustic sensors, as examples. According to various aspects, anytype of sensor may be used that allows obtaining information about therespective obstacles to be considered during analyzing the obstaclesand/or control of the vehicle.

According to various aspects, the one or more sensors 102 may beconfigured to receive (e.g., sense, detect, gather, etc.) obstacleidentification information 122 (e.g., obstacle identification data)representing one or more identification features 112 of an obstacle 111to be analyzed. The one or more identification features 112 may be alicense plate number or any other feature or group of features thatallows a (substantially) unique identification of the obstacle 111. Insome aspects, it may be sufficient if the one or more identificationfeatures 112 allow for an unambiguously identification of all obstacles111 within a predefined area, e.g., within a vicinity of a vehicle thathas a safety operation or any other driving operation implementedtherein relying on information from the obstacle analyzer 100. In thecase that the obstacle 111 is an aircraft, the one or moreidentification features 112 may include an aircraft registration numberof the aircraft. In the case that the obstacle 111 is a watercraft, theone or more identification features 112 may include a hullidentification number, as examples.

According to various aspects, the one or more sensors 102 may be furtherconfigured to receive obstacle condition information 124 associated withone or more conditions 114 of the obstacle 111. As an example, theobstacle condition information 124 may include information about one ormore characteristic features of the obstacle 111 that may be determinedand compared with predefined characteristic features.

According to some aspects, in the case that the obstacle 111 is avehicle that has no driver (e.g., if the obstacle 111 is a fullyautonomously driven vehicle), the obstacle condition information 124 mayrepresent characteristic features associated only with the vehicleitself. According to some aspects, in the case that the obstacle 111 isa vehicle that has no driver (e.g., if the obstacle 111 is a fullyautonomously driven vehicle) but one or more passengers, the obstaclecondition information 124 may represent characteristic featuresassociated with the vehicle and the one or more passengers. According tosome aspects, in the case that the obstacle 111 is a vehicle that has nodriver (e.g., if the obstacle 111 is a fully autonomously drivenvehicle) has a cargo, a trailer, etc., the obstacle conditioninformation 124 may represent characteristic features associated withthe vehicle and the cargo, a trailer, etc. According to another aspect,in the case that the obstacle 111 is a vehicle driven by a driver, theobstacle condition information 124 may represent characteristic featuresassociated with the driver of the vehicle and/or with the vehicleitself.

According to various aspects, the characteristic features may bedetected by a comparison with predefined characteristic features havinga risk value assigned thereto, e.g., included in a database. As anexample, sensor data may be checked for predefined characteristicfeatures, patterns, values, etc., and, in the case of a match, presenceof the characteristic features with respect to the analyzed obstacle maybe assumed.

According to various aspects, the one or more processors 104 may beconfigured to identify the obstacle 111 to be analyzed based on thereceived obstacle identification information 122. The one or moreprocessors 104 may be configured to generate an identification value132. The identification value 132 may be any data structure that can bestored in a computer memory and that uniquely represents a correspondingobstacle 111. In other words, the obstacle 111 to be analyzed may beunambiguously represented by the corresponding identification value 132.The identification value 132 may be anonymized to ensure data security,e.g., to avoid storage of data related to the sphere of personal privacyof the driver.

According to various aspects, the one or more processors 104 may befurther configured to determine a rating value 134 representing a riskpotential originating from the obstacle 111. In some aspects, the riskpotential of an identified obstacle 111 may be determined based on thereceived obstacle condition information 124. As an example, one or morepredefined characteristic features associated with the driver of thevehicle to be analyzed and/or with the vehicle to be analyzed may beused to evaluate the risk potential. The obstacle condition information124 may be analyzed for presence of predefined characteristic featuresand—based on the result of the analysis—to deduct a potential risk.

According to various aspects, the one or more processors 104 may beconfigured to store the rating value 134 assigned to the identificationvalue 132 of the identified obstacle 111 in one or more memories 106.The respective corresponding at least two values for each analyzedobstacle 111 may be stored, for example, in a list 116. As an example,in the case that an obstacle is detected during a safety operation orany other partially of completely autonomous driving operation iscarried out, it can be checked via the list 116 whether a rating value134 is stored for the respective obstacle. If this is the case, thesafety operation or any other partially of completely autonomous drivingfunction may be triggered and/or modified. If this is not the case, i.e.if no rating value 134 is stored for the respective obstacle, the safetyoperation or any other partially of completely autonomous drivingfunction may be carried out in a standard operation mode.

According to various aspects, the obstacle analyzer 100 may beconfigured to analyze a plurality of obstacles and store for each of theplurality of obstacles a rating value assigned to a correspondingidentification value. Illustratively, reference data are stored forvarious traffic participants that allows for an efficient riskmanagement during a travel of a vehicle that includes a control system(see, for example, FIG. 2) drawing to these reference data to adapt oneor more driving functions.

FIG. 2 illustrates a vehicle control system 200 in a schematic view,according to various aspects. The vehicle control system 200 may includeone or more sensors 202, one or more processors 204, and one or morememories 206. The one or more processors 204 and the one or morememories 206 may be part of a computing system 220. The computing system220 may be any suitable computing system implemented within a vehicle,e.g., within a motor vehicle. According to various aspects, the one ormore sensors 202 may include any sensor suitable for identifying one ormore obstacles and may be implemented within a vehicle, e.g., within amotor vehicle.

In some aspects, the obstacle analyzer 100 and the vehicle controlsystem 200 may be part of the same vehicle (see, for example, FIG. 3).In this case, the very same computing system may be used to implementthe functions of both the obstacle analyzer 100 and the vehicle controlsystem 200. Alternatively, the obstacle analyzer 100 may be not part ofthe vehicle that includes the vehicle control system 200; in this case,data may be send from the obstacle analyzer 100 to the vehicle controlsystem 200.

According to various aspects, the one or more sensors 202 of the vehiclecontrol system 200 may be configured to receive obstacle identificationinformation 222 representing one or more identification features 212 ofan obstacle 211. The obstacle 211 may be located in a vicinity of avehicle (see, for example, FIG. 3) that includes the vehicle controlsystem 200. The obstacle 211 may be, for example, another trafficparticipant, e.g., another vehicle (in particular, another motorvehicle).

According to various aspects, the one or more memories 206 of thevehicle control system 200 may include a plurality of referenceidentification values 232 and a plurality of rating values 234 storedtherein. Each of the plurality of reference identification values 232represents one (e.g., previously) identified obstacle 111 having one ofthe plurality of rating values 234 assigned thereto. As an example, theone or more memories 206 of the vehicle control system 200 may include alist 216, wherein respectively two corresponding reference values (i.e.an identification value and a rating value) are stored representing eachof a plurality of previously analyzed obstacles, see, for example,FIG. 1. In some aspects, the reference identification values 232 and thecorresponding rating values 234 may be generated by an obstacle analyzer100, as described herein. However, as an alternative, the referenceidentification values 232 and the corresponding rating values 234 may begenerated in any other suitable way.

According to various aspects, the one or more processors 204 of thevehicle control system 200 may be configured to identify the obstacle211 based on the received obstacle identification information 222 andgenerate an identification value 242 associated with the obstacle 211.The one or more processors 204 of the vehicle control system 200 may befurther configured to compare the identification value 242 with one ormore (e.g., all) of the plurality of reference identification values 232that are, for example, stored in the one or more memories 206 of thevehicle control system 200. In the case that the identification value242 matches a reference identification value 232 v of the plurality ofreference identification values 232, the one or more processors 204 ofthe vehicle control system 200 may be configure to execute at least oneof a triggering or a modification of a driving operation based on arespective rating value 234 v of the plurality of rating values 234assigned to the matching reference identification value 232 v.

As an example, the vehicle control system 200 may output a controlsignal 240 to any other device performing a driving operation and/or theone or more processors 204 of the vehicle control system 200 may beconfigured to perform the driving operation. As an example, the drivingoperation may include an autonomous cruise control (ACC) 250 that may bealso referred to as adaptive cruise control, traffic-aware cruisecontrol, automatic distance control, etc., which may automaticallyadjust the speed of the controlled vehicle to maintain a predefined(safety) distance 252 from another vehicles (e.g., from a vehicledriving ahead of the controlled vehicle). In some aspects, the one ormore processors 204 of the vehicle control system 200 may be configuredto modify 240 (or at least instruct a modification) of the predefined(safety) distance 252 of the autonomous cruise control 250.

As another example, the driving operation may include an autonomousovertaking control (AOC) 260, which may automatically control anovertake maneuver that allows the controlled vehicle to overtake anothervehicle (e.g., to overtake a vehicle driving ahead of the controlledvehicle). In some aspects, the one or more processors 204 of the vehiclecontrol system 200 may be configured to trigger a start of the overtakemaneuver. The autonomous overtaking control may further evaluate one ormore additional properties (e.g., a relative speed of the vehicle toovertake relative to the controlled vehicle, a distance to a nearestcurve, presence of sufficient free space, etc.) to ensure a safeovertake maneuver.

FIG. 3 illustrates a vehicle 300, e.g., a motor vehicle, in a schematicview, according to various aspects. The vehicle 300 may be a car, atruck, a van, a bus, or any other vehicle driving on the ground. In someaspects, the vehicle 300 may define a longitudinal axis 313. Thelongitudinal axis 313 may be associated with a forward driving direction(e.g., illustratively in direction 303 as illustrated in FIG. 3) and/ora rear driving direction (e.g., illustratively opposite to the direction303). Further, the vehicle 300 may define a lateral axis 311perpendicular to the longitudinal axis 313. According to variousaspects, the vehicle 300 may include one or more sensors 302, in someaspects one or more image sensors (e.g., one or more cameras, e.g., oneor more depth cameras, etc.). Further, the vehicle 300 may include oneor more processors 304 and one or more memories 306. The one or moreprocessors 304 and the one or more memories 306 may be part of acomputing system 320, e.g., of a head unit or a central computer of thevehicle 300. In some aspects, the computing system 320 of the vehicle300 may be configured to implement the functions of the obstacleanalyzer 100 and/or the vehicle control system 200, as described herein.

The one or more sensors 302 of the vehicle 300 may be configured toprovide sensor data 302 d (e.g., image data) to the one or moreprocessors 304 of the vehicle 300. The sensor data 302 d may represent,for example, an image (also referred to as sensor image or camera image)of a vicinity 330 of the vehicle 300. Illustratively, the sensor imagemay correspond to a field of vision 330 v (also referred to as field ofview) of the one or more sensors 302. According to various aspects, theone or more sensors 302 may be configured such that the field of vision330 v has a lateral dimension (e.g., in a horizontal plane parallel tothe lateral axis 311 and the longitudinal axis 313) and a verticaldimension (e.g., in a vertical plane perpendicular to the lateral axis311 and parallel to the longitudinal axis 313). The one or more sensors302 of the vehicle 300 may be able to receive information (e.g.,obstacle identification information 122, 222 and/or obstacle conditioninformation 124) associated with one or more objects (e.g., one or moreobstacles, e.g., one or more other vehicles, e.g., one or morepedestrians, etc.) in the vicinity 330 of the vehicle 300. In someaspects, the one or more sensors 302 may be configured to provide a 360°field of view at least in the horizontal plane.

According to various aspects, the one or more sensors 302 of the vehicle300 may include, for example, one or more cameras (e.g., one or moredepth cameras, one or more stereo cameras, etc.), one or more ultrasonicsensors, one or more radar (radio detection and ranging) sensors, one ormore lidar (light detection and ranging) sensors, etc. The one or moresensors 302 of the vehicle 300 may include, for example, any othersuitable sensor that allows a detection of an object and correspondingcharacteristic features associated with the object.

According to various aspects, the one or more sensors 302 of the vehicle300, the one or more processors 304 of the vehicle 300, and the one ormore memories 306 of the vehicle 300 described herein may implement thevehicle control system 200 and/or the obstacle analyzer 100.Alternatively, the obstacle analyzer 100 may be implemented as anadditional device and/or the vehicle control system 200 may beimplemented as an additional device. According to various aspects, thefunctions of the obstacle analyzer 100 may be implemented in adecentralized system such that the list 116, 216 may be generated fromdata of a plurality of the sensors or sensor devices located in variousdifferent locations. As an example, all traffic participants locatedwithin a pre-defined area may share the same list 116, 216.

The size of the pre-defined area may not be limited to the surroundingof the vehicle 300. As an example, a plurality of obstacle analyzers maybe used to generate a global list including all analyzed trafficparticipants. The obstacle analyzers 100 may be integrated intovehicles, as an example. However, the obstacle analyzers 100 may bealternatively mounted at any suitable infrastructure (e.g., at a trafficlight, a bridge, a traffic sign, etc.). The global list may be providedto the respective vehicle control system 200 via any type of datatransmission, e.g., based on a wireless communication.

FIG. 4 illustrates a traffic situation 400 in an exemplary schematicview, according to various aspects. In this exemplary traffic situation400, a first vehicle 402 may drive along a road 400 r and may have othervehicles 411 (that are regarded as obstacles) in its vicinity. In someaspects, the first vehicle 402 may include (or may be communicativelycoupled with) an obstacle analyzer 100 and/or a vehicle control system200. As a result, the first vehicle 402 may be able to analyze the othervehicles 411 and adapt its driving control system to a possible riskoriginating from one or more of the other vehicles 411. As an example,if a second vehicle 411-1 of the other vehicles 411 is identified by theobstacle analyzer 100 and has a rating value that represents a highpotential risk, the first vehicle 402 may either be controlled toincrease distance to this second vehicle 411-1 or to overtake the secondvehicle 411-1 provided that the traffic situation allows an overtakemaneuver.

FIG. 5 shows a schematic flow diagram of a method 500 for analyzing oneor more obstacles, according to various aspects. The method 500 mayinclude, in 510, receiving obstacle identification informationrepresenting an identification feature of an obstacle and obstaclecondition information associated with one or more conditions of theobstacle; in 520, identifying the obstacle based on the receivedobstacle identification information and generate an identification valuecorresponding to the identified obstacle; in 530, determining a ratingvalue representing a risk potential of the identified obstacle based onthe received obstacle condition information; and, in 540, storing therating value assigned to the identification value of the identifiedobstacle. According to various aspects, the method 500 may be carriedout in accordance with one or more functions as described herein withrespect to the obstacle analyzer 100.

According to various aspects, receiving obstacle identificationinformation may include generating a sensor image of a vicinity of avehicle and determining the one or more characteristic features of theobstacles from the sensor image.

FIG. 6 shows a schematic flow diagram of a method 600 for controlling avehicle, according to various aspects. The method 600 may include, in610, receiving obstacle identification information representing anidentification feature of an obstacle in a vicinity of a vehicle; in620, identifying the obstacle based on the received obstacleidentification information and generating an identification valuecorresponding to the obstacle; in 630, comparing the identificationvalue with one or more reference identification values of a plurality ofreference identification values, the plurality of referenceidentification values representing a plurality of previously identifiedobstacles, each of the plurality of reference identification valueshaving a rating value assigned thereto; and, in 640, in the case thatthe identification value matches a reference identification value of theplurality of reference identification values, executing at least one ofa triggering or a modification of a driving operation based on therating value assigned to the reference identification value. Accordingto various aspects, the method 600 may be carried out in accordance withone or more functions as described herein with respect to the vehiclecontrol system 200.

In the following, various aspects are described in more detail. Theconfigurations and functions described below may be implemented in theobstacle analyzer 100 and/or the vehicle control system 200 as describedabove. The configurations and functions described below may be part ofthe method 500 or method 600 as described above.

Various aspects are based on an observation of one or more componentsregarding traffic in a vicinity of a vehicle. The one or more componentsmay include: inherent properties of nearby vehicles (e.g., make & model,tire tread, etc.), a driver analysis including an indirect analysis viaobservations of patterns in the vehicle behavior and/or a directanalysis based on a visual observation of the driver (e.g., of thedrivers face and/or body), cargo and/or passenger loading, etc.

In some cases, predefined properties may not be fully accessible due toa limited field of view of the respective sensors that are used for theanalysis, e.g., the obstacles may be analyzed from the first vehicles402 point of view, as illustrated in FIG. 4. In this case, it may bedifficult to estimate whether the engine hood of the second vehicle411-1 driving in front of the first vehicle 402 has any damages or othercharacteristic features. Therefore, in some aspects, one or morecommunication mechanisms (e.g., vehicle to vehicle (V2V), infrastructureto vehicle (X2V)), etc.) may be used where other vehicles orvision-based systems are able to report data (e.g., including obstacleidentification information and/or obstacle condition information) to thefirst vehicle 402. Via the one or more communication mechanisms, one ormore obstacles (e.g., other vehicles that may be relevant for thetraffic situation) may be analyzed that may be not (or not completely)visible from the position of the first vehicle 402 that may use theobtained information. With advances in V2V and V2X communication, theanalysis of the one or more obstacles may become more and moreefficient.

According to various aspects, an analysis of at least one other vehicle411 around the first vehicle 402 may be performed. As mentioned above,information reported from other entities may be combined for theanalysis, if available and desired. To assess the properties of anothervehicle 411, it may be identified which exact make and model the othervehicle 411 is. Using, for example, object recognition with trainedpatterns on existing and known cars may provide this.

Further, through vision-based methods and information sent to the firstvehicle from other vehicles, key properties of the other vehicle may beestimated. Key properties may be at least one of: tire status, treaddepth (New or worn out?), proper tire type for the prevailing weatherconditions (e.g., if there are snowy roads, is the other vehicleequipped with winter tires, since summer tires would have much worsetraction and increased stopping distance), snow chains, etc.

FIG. 7A shows exemplarily an image 700 a of a part of a winter tireincluding one or more characteristic features. As an example, a wintertire may be detected by analyzing the image 700, e.g., by check for apresence of one or more characteristic features, as for example apictogram 702 and/or a tread pattern 704 that may be typical for wintertires.

FIG. 7B shows exemplarily an image 700 b of a part of a tire of a carincluding one or more characteristic features. As an example, a treaddepth of the tire may be estimated by analyzing the image 700 b, e.g.,by check for a presence of one or more characteristic features, as forexample a tread pattern 706 that may be typical for new tires and/orworn out tires.

FIG. 7C shows exemplarily an image 700 c of a wheel of a car includingone or more characteristic features. As an example, a shape of the tiremay be estimated by analyzing the image 700 c, e.g., via determiningpresence of one or more characteristic features, as for example apattern 708 representing a flat tire, i.e. a tire with not sufficientair pressure. In some aspects, a low profile rim may be detected. Sincedetermining a flat tire may result in too many false positives for lowprofile rims, the determination of a flat tire may be discarded in thiscase.

As show exemplarily above, various characteristic features may bedetected vision-based. As another example, one or more damages of avehicle may be detected, e.g., vision-based and/or assisted by usingpredefined models of the vehicles for comparison. In some aspects, thecondition of the outer skin of the other vehicle may be determined, asexemplarily illustrated in FIG. 8. FIG. 8 illustrates various images 800a, 800 b, 800 c, 800 d of another vehicle including one or morecharacteristic features 802 representing damages. As examples, it may bedetermined whether dents or scrapes are present at the other vehicle,whether items of the other vehicle are held together through duct tape,or whether other visible damages are present. Dents, scrapes, and ducttape may be detected through object recognition, for example.

According to various aspects, a more sophisticated method may involveanalyzing depth/stereo images from depth/stereo cameras.

As an example, using depth/stereo images, a 3D model of the othervehicle can be reconstructed on the fly and may be compared to referencemodels. One example of using depth and gray scale information isillustrated in FIG. 9. FIG. 9 shows an image 900 a of a car, a depthimage 900 b of a car, and a 3D reference model 900 c to analyze make andmodel based on a comparison of the image data with the reference model900 c. As another example, make and model specific features may bedetected from the image 900 a.

According to various aspects, one or more car properties may be analyzedbased on make and model. In some aspects, perspective distortions thatmay be present in the images may be removed (in other words compensated)through calculations. In the case that, for example, the correct tiretype in reasonable condition is detected, the car's properties (e.g.:How many meters to stop when fully braking in dry street conditions? Howlong to stop in rainy conditions? How fast can the other caraccelerate?) at the current weather may be estimated based on knowledgeabout make and model of the car. If a human driver is assumed for theother vehicle, e.g., the other car does not exist with systems forautonomous driving, an average reaction time of human drivers may beconsidered as well for such estimations.

In some aspects, a make and, e.g. optionally, a model of a vehicle maybe visually identified due to the distinctive taillights, front lights,or other characteristic features. However, it may be also possible toread the make and model badging 902 that may be usually located on thetail lid of a vehicle.

According to various aspects, a bumper height of the other vehicle maybe analyzed. It may be helpful, to avoid especially a decapitation dueto a collision with a rear-end of a larger vehicle (e.g., a truck) or ofa lifted pick-up. According to various aspects, it may be checkedwhether a car has been significantly modified from stock. Therefore, acar to be analyzed may be compared to a corresponding default car model.As an example, a difference in a rim size, a tweaked spoiler, asuspension slammed to ground, etc., may be noticed.

According to various aspects, stickers on a car may be detected and itscontent may be analyzed. Some car owners may present their opinion inthe form of stickers on the back of the car. While this might not be a100% sure indicator as someone else might be driving the car, it may bestill considered during a risk potential estimation what type ofstickers may be attached to a car. The content of various car stickersthat might indicate that we should keep a larger safety distance may beconsidered as input data for the analysis. As an example, a stickerhaving, for example, the content “driving school”, “student driver”,“long vehicle”, “heavy load”, “taxi”, etc., may be actually designed towarn or inform other traffic members.

According to various aspects, a driver profile of one or more othervehicle may be analyzed. Besides an analysis of the one or more othervehicles itself, an insight may be obtained from generating a temporaryprofile of a driver of another vehicle. Ethical and moral implicationsof generating a driver profile may be considered. As an example, societyexpects equal treatment of every person without regard to physicalappearances, and this may be acknowledged in the obstacle analyzationdescribed herein. Any discrimination is avoided in which someone mighthave fewer rights than another person based on their appearance and thelike. However, taking, for example, a driver's age into account may beappropriate as it may be often a proxy for both behind-the-wheel drivingexperience as well as health conditions that adversely affect reflexesand driving ability. While this is a very philosophical topic andmultiple answers might be correct, it may be accepted by the society atleast in some countries that creating a temporary driver profile toadjust autonomous driving can be useful.

As an example, the functions described herein may be configured in sucha way, that no discrimination occurs against any person based on such agenerated driver profile, since none of the driver's rights are limiteddue to the proposed use of such profile. As an example, more safetydistance may be kept to possibly avoid potential accidents, thereforelives may be saved. However, none of the functions implemented in theobstacle analyzer 100 or the vehicle control system 200 may take awayanyone's right to turn when it is given; and no driver is limited in hisfreedom by taking precautions. According to various aspects, thedriver's profile may be generated and stored only temporarily andanonymously. Once the other vehicle is, for example, far enough away,and/or a predefined time is passed, the stored data may be discarded.

The analysis of the driver's profile may include one or more of thefollowing aspects. According to various aspects, any available visualinformation about a driver of a vehicle may be used to generate adriver's profile, or in other words, to estimate a risk potentialoriginated from the behavior or status of the driver. The informationmay be obtained either from one or more sensors (e.g., cameras) of thefirst vehicle 402 (see, for example, FIG. 4) or form other cars orstreet cameras. The following characteristics of the other driver may beobserved:

-   -   A driver's mouth may indicate whether the driver is engaged in a        conversation or not.    -   A driver may hold a cell phone or a tablet.    -   A Driver's head may turns wildly/repeatedly or to the side for        prolonged period of time which may indicate that the driver may        not pay attention to the road ahead.    -   A driver's head may bounce, e.g., to loud music detected through        one or more microphones.    -   Other objects may be in the driver's hand, such as a drink,        food, etc.    -   Elderly driver may statistically tend to have slower reflexes        and less acute vision. Conversely, teen drivers may be        statistically the most accident-prone drivers (e.g., as        reflected in insurance rates at least in some countries).

Further, in terms of the driving behavior of the driver, followingpatterns may be observed:

-   -   Repeated lane changes, weaving through traffic; every lane        change carries an additional risk.    -   Tailgating.    -   Cutting off of driver during lane change.    -   Abrupt application of brakes or acceleration.    -   Failure to use turn signals.    -   Over-revving engine.    -   Squealing tires.    -   Drifting to outside lane or to edge of lane.    -   Swerving repeatedly inside the own lane.

As another example, cargo & passengers of the other vehicle may beobserved. As an example, following situations may be observed:

-   -   Children may distract the driver.    -   The vehicle may be heavily loaded. This may detract from        standard performance both in terms of braking ability and        emergency maneuvers.    -   The vehicle may tow a trailer.    -   The vehicle may have unsecured or poorly secured objects in its        pickup bed or hatch/trunk.    -   Passengers may throw objects out of the vehicle.    -   A passenger or the driver may lean out the window.    -   A passenger or the driver may shout out the window.

As another example, there may be a situation where a driver or passengerof a vehicle is pointing a weapon (e.g. a gun) out of a window of thevehicle. This may be associated with a specific safety operation, e.g.an emergency stop.

In general, according to various aspects, any typical situation orstatistical information that may increase a risk potential associatedwith another vehicle may be mapped to specific features that can belooked for via one or more suitable sensors. If a vehicle's profile,driver's profile, passenger's profile, etc., is generated and/orevaluated, a risk value may be determined that represents the sum of allrisks determined from the profiles.

Many of the mentioned issues might raise concern that guides the vehicleto keep a larger safety distance than usual. In the opposite way, thevehicle might in some instances keep a lower safety distance than usual,but still within safe parameters to assure that the vehicle can performa full stop in case of unpredictable events. In other cases, reactionsmight include yielding the right of way. For example, two cars may turnleft on opposite street sides, and one car has to behave defensively andgive up the right to take the left turn, so the other vehicle can passand the vehicle can pass afterwards. Detecting that the other vehiclehas, for example, a less experienced driver might play a role inyielding the right of way to cope with uncertainty and risk.

Similarly, it may be useful to not be followed by a high riskdriver/vehicle. One option may be to increase speed to widen the gap.However, if speed limits or traffic conditions preclude this or if thefollowing vehicle matches our speed, other options may be considered,such as changing lanes if there are multiple travel lanes, and/orletting a trailing vehicle pass; when the vehicle is behind the othervehicle, the vehicle can control the safety buffer. Just like whenclimbing a mountain, someone may always want to keep the inexperiencedpersons below you. By exercising these defensive driving techniques, theprobability of accidents may be decreased.

In general, there may be a need for a greater following distance underhigh-risk situations (such as the other driver texting, etc.) since theusually planned braking distance may not be sufficient in somescenarios, e.g. in the case of an accident, etc. As an example, aconventional braking distance models may be insufficient in the casethat another vehicle in front of a vehicle slams into a parked vehicledue to distraction. In this case, there will be, for example, no brakelights and the braking distance may be effectively zero regardless ofthe make and model. In some aspects, such high-risk situations aredetected in advance; and the following distances may be increased to asufficient distance.

To determine whether the vehicle may decide to hold a larger drivingdistance, several other factors may be taken into account, which may berelevant, e.g.: weather condition (rain, fog, dry, snowy . . . ), roadconditions, health of the vehicle (the first vehicle 402), tire statusof the vehicle (the first vehicle 402), etc.

FIG. 10A, FIG. 10B, and FIG. 10C show exemplarily various flow diagramsrelated to one or more aspects described herein. As a first example,other vehicles 411 around a first vehicle 402 (also referred to as orthe vehicle that contains the obstacle analyzer 100 and/or the vehiclecontrol system 200) may be analyzed 1000 a, as illustrated in FIG. 10Ain a schematic view. As a second example, drivers of other vehicles 411around a first vehicle 402 may be analyzed 1000 b, as illustrated inFIG. 10B in a schematic view. As a third example, passengers and/orcargo in other vehicles 411 around a first vehicle 402 may be analyzed1000 c, as illustrated in FIG. 10C in a schematic view. It has to beunderstood that various functions illustrated in the figures may beoptional features.

In the following, various details are provided referring a possiblecalculation of the rating value or a value that represents a riskpotential originated from another vehicle.

FIG. 11 shows a flow chart 1100 for calculating a vehicle risk score.According to various aspects, the vehicle risk score may be the ratingvalue or at least a part of a rating value. FIG. 12 shows a flow chart1200 for calculating a driver risk score. According to various aspects,the driver risk score may be the rating value or at least a part of arating value. FIG. 13 shows a flow chart 1300 for determining an action.In some aspects, the vehicle risk score and the driver risk score may beput together to determine the action.

As illustrated in FIG. 11, each other vehicle in a vicinity of a vehiclemay have an assessed risk (represented by a numerical risk value)starting at zero points. When the cumulative risk exceeds a series ofprogressive thresholds, appropriate counter-measures may be taken. Twoexemplary risk index databases are shown in FIG. 11. The respective riskindex database associates, for example, a predefined amount of riskpoints to various predefined characteristic features, e.g., to a damagetype or a tread depth value. In a similar, way, various risk indexdatabases may be used to evaluate risk values for predefined vehicleconditions, e.g., predefined properties of the other vehicle.

For the vehicle risk, the tire tread depth and environmental conditionsmay be considered in combination. For example, a bald tire works justfine in dry conditions.

As illustrated in FIG. 12, a driver risk may be analyzed in a similarway using various risk databases. The driver risk may be reset after apredefined period of time (e.g., after 60 minutes). Otherwise, the modelmay likely deem all drivers to be risky after following them for severalhours.

According to various aspects, the databased that may be used todetermine the rating value associated with the risk potential of theanalyzed obstacle may include an artificial intelligence to estimatecertain properties of the vehicle, the driver, the passengers, etc.

For determining a course of action, as for example illustrated in FIG.13, it may be considered whether or not the vehicle is on course to passthe other vehicle being assessed. If this is not the case, the vehiclecan simply increase its following distance to the other vehicle based onthe total risk profile of the other vehicle. If it is possible to passthe other vehicle (to overtake) the vehicle may consider whether tocontinue to pass the vehicle or abort. There may be three parametersinvolved in this decision: the risk score of the vehicle to overtake,the time until the next turn, and the speed difference between the twovehicles.

If the vehicle is scheduled to turn in a short distance (e.g., a mile),it may be no option to pass the other vehicle, even if a high-risk valuewas evaluated for the other vehicle. On the other hand, if the vehicleis scheduled to continue for a long distance (e.g., 100 miles) and theother vehicle is driving, for example, 10 mph slower than the vehicle,the vehicle may pass the other vehicle, e.g., unless a very high risk isevaluated for the performance of the overtaking maneuver.

In the following, various examples are provided that are related to theaspects described above and illustrated in the figures.

Example 1 is an obstacle analyzer, including: one or more sensorsconfigured to receive obstacle identification information representingone or more identification features of an obstacle and obstaclecondition information associated with one or more conditions of theobstacle; and one or more processors configured to identify the obstaclebased on the received obstacle identification information and generatean identification value corresponding to the identified obstacle,determine a rating value representing a risk potential of the identifiedobstacle based on the received obstacle condition information, and storethe rating value assigned to the identification value of the identifiedobstacle in one or more memories.

In Example 2, the obstacle analyzer of Example 1 may optionally furtherinclude that the obstacle is a vehicle and wherein the identificationfeature is a unique vehicle identifier.

In Example 3, the obstacle analyzer of Example 1 or 2 may optionallyfurther include that the obstacle is a motor vehicle and wherein theidentification feature is a license plate number of a license plate ofthe motor vehicle.

In Example 4, the obstacle analyzer of any one of Examples 1 to 3 mayoptionally further include that the determination of the rating valueincludes selecting the rating value from a predefined set of ratingvalues based on the received obstacle condition information. Thepredefined set of rating values may include at least two distinct riskpotential tiers.

In Example 5, the obstacle analyzer of any one of Examples 1 to 4 mayoptionally further include that the obstacle is a vehicle and whereinthe determination of the rating value includes checking the receivedobstacle condition information for pre-defined characteristic featuresof the vehicle itself. The rating value may be generated based onrespective vehicle risk values corresponding to the pre-definedcharacteristic features associated with the vehicle.

In Example 6, the obstacle analyzer of any one of Examples 1 to 4 mayoptionally further include that the obstacle is a completelyautonomously driven vehicle, and that the determination of the ratingvalue includes checking the received obstacle condition information forpre-defined characteristic features of the vehicle.

In Example 7, the obstacle analyzer of any one of Examples 1 to 4 mayoptionally further include that the obstacle is a vehicle driven by adriver, and that the determination of the rating value includes checkingthe received obstacle condition information for pre-definedcharacteristic features of the driver of the vehicle. The rating valuemay be generated based on respective driver risk values corresponding tothe pre-defined characteristic features associated with the driver.

In Example 8, the obstacle analyzer of Example 6 or 7 may optionallyfurther include that the pre-defined characteristic features of thevehicle include one or more operational status features representing anoperational state of the vehicle.

In Example 9, the obstacle analyzer of Example 8 may optionally furtherinclude that the one or more operational status features include atleast one of the following features: tire tread of tires, season-type oftires, damages of the vehicle, at least one of smoke or dust generatedby the vehicle, a loading of the vehicle, presence of a trailerconnected to the vehicle, size of the vehicle, presence of an underrideguard attached to the vehicle, fluttering parts of the vehicle.

In Example 10, the obstacle analyzer of any one of Examples 6 to 9 mayoptionally further include that the pre-defined characteristic featuresof the vehicle include one or more characteristic make and/or modelfeatures representing a make and/or model of the vehicle.

In Example 11, the obstacle analyzer of any one of Examples 6 to 10 mayoptionally further include that pre-defined characteristic features ofthe vehicle include one or more tune-up features representing a tune-upof the vehicle.

In Example 12, the obstacle analyzer of any one of Examples 6 to 11 mayoptionally further include that the pre-defined characteristic featuresof the vehicle include one or more driving patterns representing adriving behavior associated with the vehicle in traffic.

In Example 13, the obstacle analyzer of Example 12 may optionallyfurther include that the one or more driving patterns represent at leastone of the following: a lane shifting behavior, an accelerationbehavior, a braking behavior, a tailgating or safety distance keepingbehavior, a traffic sign observance behavior, a lane keeping behavior.

In Example 14, the obstacle analyzer of any one of Examples 6 to 13 mayoptionally further include that the pre-defined characteristic featuresof the vehicle include content of one or more stickers on the vehicle.

In Example 15, the obstacle analyzer of Example 6 may optionally furtherinclude that the pre-defined characteristic features of the driverinclude one or more characteristic distraction features representing oneor more situations where the driver of the vehicle is distracted fromobserving traffic.

In Example 16, the obstacle analyzer of Example 15 may optionallyfurther include that the one or more situations where the driver of thevehicle is distracted from observing traffic includes at least one ofthe following situations: the driver reading a book, driver listening tomusic, the driver eating or drinking, the driver using an electroniccommunication device, the driver observing one or more displays insidethe vehicle, the driver sleeping, the driver being unconscious, thedriver interacting with another passenger in the vehicle.

In Example 17, the obstacle analyzer of any one of Examples 1 to 16 mayoptionally further include that the determination of the rating valuefurther includes determining a current weather condition and consideringthe current weather condition during the generation of the rating value.

In Example 18, the obstacle analyzer of any one of Examples 1 to 17 mayoptionally further include that the rating value assigned to thecorresponding identified obstacle is reduced or reset after a predefinedtime.

In Example 19, the obstacle analyzer of any one of Examples 1 to 18 mayoptionally further include that the one or more sensors include one ormore cameras and that at least one of the identification of the obstacleor the determination of the rating value is based on an image analysisof one or more images obtained by the one or more cameras.

In Example 20, the obstacle analyzer of any one of Examples 1 to 18 mayoptionally further include that the one or more sensors include one ormore receivers and that at least one of the identification of theobstacle or the determination of the rating value is based on datareceived by the one or more receivers.

Example 21 is a method for analyzing one or more obstacles, the methodincluding: receiving obstacle identification information representing anidentification feature of an obstacle and obstacle condition informationassociated with one or more conditions of the obstacle; identifying theobstacle based on the received obstacle identification information andgenerate an identification value corresponding to the identifiedobstacle; determining a rating value representing a risk potential ofthe identified obstacle based on the received obstacle conditioninformation; and storing the rating value assigned to the identificationvalue of the identified obstacle.

In Example 22, the method of Example 21 may optionally further includethat the obstacle is a vehicle and wherein the identification feature isa unique vehicle identifier.

In Example 23, the method of Example 21 or 22 may optionally furtherinclude that the obstacle is a motor vehicle and that the identificationfeature is a license plate number of a license plate of the motorvehicle.

In Example 24, the method of any one of Examples 21 to 23 may optionallyfurther include that determining the rating value includes selecting therating value from a predefined set of rating values based on thereceived obstacle condition information. The predefined set of ratingvalues may include at least two distinct risk potential tiers.

In Example 25, the method of any one of Examples 21 to 24 may optionallyfurther include that the obstacle is a vehicle and wherein determiningthe rating value includes checking the received obstacle conditioninformation for pre-defined characteristic features of the vehicleitself. The rating value may be generated based on respective vehiclerisk values corresponding to the pre-defined characteristic featuresassociated with the vehicle.

In Example 26, the method of any one of Examples 21 to 24 may optionallyfurther include that the obstacle is a completely autonomously drivenvehicle, and that determining the rating value includes checking thereceived obstacle condition information for pre-defined characteristicfeatures of the vehicle.

In Example 27, the method of any one of Examples 21 to 24 may optionallyfurther include that the obstacle is a vehicle driven by a driver, andthat determining the rating value includes checking the receivedobstacle condition information for pre-defined characteristic featuresof the driver of the vehicle. The rating value may be generated based onrespective driver risk values corresponding to the pre-definedcharacteristic features associated with the driver.

In Example 28, the method of Example 26 or 27 may optionally furtherinclude that the pre-defined characteristic features of the vehicleinclude one or more operational status features representing anoperational state of the vehicle.

In Example 29, the method of Example 28 may optionally further includethat the one or more operational status features include at least one ofthe following features: tire tread of tires, season-type of tires,damages of the vehicle, at least one of smoke or dust generated by thevehicle, a loading of the vehicle, presence of a trailer connected tothe vehicle, size of the vehicle, presence of an underride guardattached to the vehicle, fluttering parts of the vehicle.

In Example 30, the method of any one of Examples 26 to 29 may optionallyfurther include that the pre-defined characteristic features of thevehicle include one or more characteristic make and/or model featuresrepresenting a make and/or model of the vehicle.

In Example 31, the method of any one of Examples 26 to 30 may optionallyfurther include that the pre-defined characteristic features of thevehicle include one or more tune-up features representing a tune-up ofthe vehicle.

In Example 32, the method of any one of Examples 26 to 31 may optionallyfurther include that the pre-defined characteristic features of thevehicle include one or more driving patterns representing a drivingbehavior associated with the vehicle in traffic.

In Example 33, the method of Example 32 may optionally further includethat the one or more driving patterns represent at least one of thefollowing: a lane shifting behavior, an acceleration behavior, a brakingbehavior, a tailgating or safety distance keeping behavior, a trafficsign observance behavior, a lane keeping behavior.

In Example 34, the method of any one of Examples 26 to 33 may optionallyfurther include that the pre-defined characteristic features of thevehicle include content of one or more stickers on the vehicle.

In Example 35, the method of Example 26 may optionally further includethat the pre-defined characteristic features of the driver include oneor more characteristic distraction features representing one or moresituations where the driver of the vehicle is distracted from observingtraffic.

In Example 36, the method of Example 35 may optionally further includethat the one or more situations where the driver of the vehicle isdistracted from observing traffic includes at least one of the followingsituations: the driver reading a book, driver listening to music, thedriver eating or drinking, the driver using an electronic communicationdevice, the driver observing one or more displays inside the vehicle,the driver sleeping, the driver being unconscious, the driverinteracting with another passenger in the vehicle.

In Example 37, the method of any one of Examples 21 to 36 may optionallyfurther include that determining the rating value further includesdetermining a current weather condition and considering the currentweather condition during the generation of the rating value.

In Example 38, the method of any one of Examples 21 to 37 may optionallyfurther include that the rating value assigned to the correspondingidentified obstacle is reduced or reset after a predefined time.

In Example 39, the method of any one of Examples 21 to 38 may optionallyfurther include that one or more cameras may be used to receive theobstacle identification information and/or the obstacle conditioninformation and that at least one of identifying the obstacle ordetermining the rating value is based on an image analysis of one ormore images obtained by the one or more cameras.

In Example 40, the method of any one of Examples 21 to 38 may optionallyfurther include that one or more receivers may be used to receive theobstacle identification information and/or the obstacle conditioninformation and that at least one of identifying the obstacle ordetermining the rating value is based on data received by the one ormore receivers.

Example 41 is a vehicle control system, including: one or more sensorsconfigured to receive obstacle identification information representingan identification feature of an obstacle in a vicinity of a vehicle; oneor more memories including a plurality of reference identificationvalues representing a plurality of previously identified obstacles, eachof the plurality of reference identification values having a ratingvalue assigned thereto; and one or more processors configured toidentify the obstacle based on the received obstacle identificationinformation and generate an identification value, compare theidentification value with one or more of the plurality of referenceidentification values, and, in the case that the identification valuematches a reference identification value of the plurality of referenceidentification values, (executing at least one of a triggering or amodification of) at least one of trigger or modify a driving operationbased on the rating value assigned to the reference identificationvalue.

In Example 42, the vehicle control system of Example 41 may optionallyfurther include that the driving operation includes keeping a predefinedsafety distance, and that the modification of the driving operationincludes increasing the predefined safety distance in the case that therating value is at a predefined threshold.

In Example 43, the vehicle control system of Example 41 may optionallyfurther include that the driving operation includes an overtakingmaneuver to overtake the vehicle.

In Example 44, the vehicle control system of Example 43 may optionallyfurther include that the overtaking maneuver includes determining arelative speed of the vehicle to be overtaken and a free space toovertake the vehicle.

Example 45 is a method for controlling a vehicle, the method including:receiving obstacle identification information representing anidentification feature of an obstacle in a vicinity of a vehicle;identifying the obstacle based on the received obstacle identificationinformation and generating an identification value corresponding to theobstacle; comparing the identification value with one or more referenceidentification values of a plurality of reference identification values,the plurality of reference identification values representing aplurality of previously identified obstacles, each of the plurality ofreference identification values having a rating value assigned thereto;and, in the case that the identification value matches a referenceidentification value of the plurality of reference identificationvalues; and executing at least one of a triggering or a modification ofa driving operation based on the rating value assigned to the referenceidentification value.

In Example 46, the method of Example 45 may optionally further includethat the driving operation includes keeping a predefined safetydistance, and that the modification of the driving operation includesincreasing the predefined safety distance in the case that the ratingvalue is at a predefined threshold.

In Example 47, the method of Example 45 may optionally further includethat the driving operation includes an overtaking maneuver to overtakethe vehicle.

In Example 48, the method of Example 47 may optionally further includethat the overtaking maneuver includes determining a relative speed ofthe vehicle to be overtaken and a free space to overtake the vehicle.

In Example 49, the method of any one of Examples 45 to 48 may optionallyfurther include that the vehicle is a car.

In Example 50, the method of Example 49 may optionally further includethat the identification feature is a license plate number of the car.

Example 51 is a vehicle, including an obstacle analyzer according to anyone of Examples 1 to 20. Further, the one or more processors of theobstacle analyzer may be configured to identify a current obstacle basedon the received obstacle identification information and generate acurrent identification value, compare the current identification valuewith the identification value stored in the one or more memories, and,in the case that the current identification value matches the storedidentification value, at least one of trigger or modify a drivingoperation based on the stored rating value assigned to the storedidentification value.

Example 52 is a vehicle control system, including: one or more sensorsconfigured to receive current obstacle identification informationrepresenting an identification feature of an obstacle in a vicinity of avehicle; one or more memories including stored risk values assigned tostored identification values of a plurality of previously identifiedobstacles; one or more processors configured to identify the obstaclebased on the received current obstacle identification information, checkwhether a risk value is stored in the one or more memories for theidentified obstacle, and, in the case that a risk value is stored, atleast one of trigger or modify a driving operation based on the riskvalue.

Example 53 is a vehicle analyzer, including: one or more sensorsconfigured to receive vehicle identification information representing anidentification feature of an vehicle, vehicle condition informationassociated with one or more conditions of the vehicle, and driverinformation associated with a driver of the vehicle; one or moreprocessors configured to determine vehicle risk data representing a riskpotential of the vehicle based on the received identificationinformation and the received vehicle condition information, determinedriver risk data representing a risk potential of the driver of thevehicle based one the received driver information and the receivedvehicle condition information, determine a total risk value from thedetermined vehicle risk data and the determined driver risk data, setone or more first driving operation parameters for a driving operationin the case that the total risk value is below a predefined threshold,set one or more second driving operation parameters for the drivingoperation in the case that the total risk value is above a predefinedthreshold.

Example 54 is an obstacle analyzer, including: one or more sensorsconfigured to receive obstacle identification information representingan identification feature of an obstacle and obstacle conditioninformation associated with one or more conditions of the obstacle; acomputing system configured to determine risk data representing a riskpotential of the obstacle, the determination of the risk data is basedon the received obstacle identification information and the receivedobstacle condition information, and store the risk data for theobstacle.

Example 55 is an obstacle analyzer, including: one or more sensors, oneor more processors, and one or more memories; the one or more sensorsare configured to receive identification information of a plurality ofobstacles and the one or more processors are configured to identify eachobstacle of the plurality of obstacles based on the receivedidentification information; the one or more sensors are furtherconfigured to receive condition information associated with one or moreconditions of each identified obstacle; the one or more processors arefurther configured to determine a risk value for each identifiedobstacle representing a risk potential of the respective obstacle basedon the received obstacle condition information, and store the risk valuefor each identified obstacle.

While the disclosure has been particularly shown and described withreference to specific aspects, it should be understood by those skilledin the art that various changes in form and detail may be made thereinwithout departing from the spirit and scope of the disclosure as definedby the appended claims. The scope of the disclosure is thus indicated bythe appended claims and all changes, which come within the meaning andrange of equivalency of the claims, are therefore intended to beembraced.

What is claimed is:
 1. An obstacle analyzer, comprising: one or moresensors configured to receive obstacle identification informationrepresenting one or more identification features of an obstacle andobstacle condition information associated with one or more conditions ofthe obstacle; and one or more processors configured to identify theobstacle based on the received obstacle identification information andgenerate an identification value corresponding to the identifiedobstacle, determine a rating value representing a risk potential of theidentified obstacle based on the received obstacle conditioninformation, and store the rating value assigned to the identificationvalue of the identified obstacle in one or more memories.
 2. Theobstacle analyzer of claim 1, wherein the obstacle is a vehicle andwherein the identification feature is a unique vehicle identifier. 3.The obstacle analyzer of claim 1, wherein the obstacle is a motorvehicle and wherein the identification feature is a license plate numberof a license plate of the motor vehicle.
 4. The obstacle analyzer ofclaim 1, wherein the determination of the rating value comprisesselecting the rating value from a predefined set of rating values basedon the received obstacle condition information, wherein the predefinedset of rating values comprises at least two distinct risk potentialtiers.
 5. The obstacle analyzer of claim 1, wherein the obstacle is avehicle and wherein the determination of the rating value compriseschecking the received obstacle condition information for pre-definedcharacteristic features of the vehicle itself, wherein the rating valueis generated based on respective vehicle risk values corresponding tothe pre-defined characteristic features associated with the vehicle. 6.The obstacle analyzer of claim 1, wherein the obstacle is a completelyautonomously driven vehicle, and wherein the determination of the ratingvalue comprises checking the received obstacle condition information forpre-defined characteristic features of the vehicle.
 7. The obstacleanalyzer of claim 1, wherein the obstacle is a vehicle driven by adriver, and wherein the determination of the rating value compriseschecking the received obstacle condition information for pre-definedcharacteristic features of the driver of the vehicle, wherein the ratingvalue is generated based on respective driver risk values correspondingto the pre-defined characteristic features associated with the driver.8. The obstacle analyzer of claim 6, wherein the pre-definedcharacteristic features of the vehicle comprise one or more operationalstatus features representing an operational state of the vehicle.
 9. Theobstacle analyzer of claim 6, wherein the pre-defined characteristicfeatures of the vehicle comprise one or more characteristic make and/ormodel features representing a make and/or model of the vehicle.
 10. Theobstacle analyzer of claim 6, wherein the pre-defined characteristicfeatures of the vehicle comprise one or more driving patternsrepresenting a driving behavior associated with the vehicle in traffic.11. The obstacle analyzer of claim 6, wherein the pre-definedcharacteristic features of the vehicle comprise content of one or morestickers on the vehicle.
 12. The obstacle analyzer of claim 6, whereinthe pre-defined characteristic features of the driver comprise one ormore characteristic distraction features representing one or moresituations where the driver of the vehicle is distracted from observingtraffic.
 13. The obstacle analyzer of claim 1, wherein the determinationof the rating value further comprises determining a current weathercondition and considering the current weather condition during thegeneration of the rating value.
 14. The obstacle analyzer of claim 1,wherein the rating value assigned to the corresponding identifiedobstacle is reduced or reset after a predefined time.
 15. The obstacleanalyzer of claim 1, wherein the one or more sensors comprise one ormore cameras and wherein at least one of the identification of theobstacle or the determination of the rating value is based on an imageanalysis of one or more images obtained by the one or more cameras. 16.The obstacle analyzer of claim 1, wherein the one or more sensorscomprise one or more receivers and wherein at least one of theidentification of the obstacle or the determination of the rating valueis based on data received by the one or more receivers.
 17. A vehiclecontrol system, comprising: one or more sensors configured to receiveobstacle identification information representing an identificationfeature of an obstacle in a vicinity of a vehicle; one or more memoriescomprising a plurality of reference identification values representing aplurality of previously identified obstacles, each of the plurality ofreference identification values having a rating value assigned thereto;and one or more processors configured to identify the obstacle based onthe received obstacle identification information and generate anidentification value, compare the identification value with one or moreof the plurality of reference identification values, and, in the casethat the identification value matches a reference identification valueof the plurality of reference identification values, (executing at leastone of a triggering or a modification of) at least one of trigger ormodify a driving operation based on the rating value assigned to thereference identification value.
 18. The vehicle control system of claim17, wherein the driving operation comprises keeping a predefined safetydistance, and wherein the modification of the driving operationcomprises increasing the predefined safety distance in the case that therating value is at a predefined threshold.
 19. The vehicle controlsystem of claim 17, wherein the driving operation comprises anovertaking maneuver to overtake the vehicle.
 20. A vehicle, comprising:an obstacle analyzer, the obstacle analyzer comprising one or moresensors configured to receive obstacle identification informationrepresenting one or more identification features of an obstacle andobstacle condition information associated with one or more conditions ofthe obstacle; and the obstacle analyzer further comprising one or moreprocessors configured to identify the obstacle based on the receivedobstacle identification information and generate an identification valuecorresponding to the identified obstacle, determine a rating valuerepresenting a risk potential of the identified obstacle based on thereceived obstacle condition information, and store the rating valueassigned to the identification value of the identified obstacle in oneor more memories; wherein the one or more processors of the obstacleanalyzer are further configured to identify a current obstacle based onthe received obstacle identification information and generate a currentidentification value, compare the current identification value with theidentification value stored in the one or more memories, and, in thecase that the current identification value matches the storedidentification value, at least one of trigger or modify a drivingoperation based on the stored rating value assigned to the storedidentification value.